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Life Cycle Inventory Analysis (LCIA)



The Boulevard Hotel, Kuala Lumpur, 4
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Life Cycle Assessment & Life Cycle Management Methodologies
Life Cycle Inventory Analysis (LCIA)
Dr B V Babu, Professor
Assistant Dean – Engineering Services Division &
Head – Chemical Engineering Department
Birla Institute of Technology and Science (BITS)
PILANI – 333 031 (Rajasthan) India
1. Introduction 3
2. Life Cycle Inventory Analysis (LCIA) 4
2.1. Raw Materials Acquisition 6
2.2. Manufacture and Fabrication 7
2.3. Transportation/Distribution 8
2.4. Consumer Use/Disposal 9
2.5. Recycling 9
3. Key Steps in Life Cycle Inventory Analysis 11
3.1. Develop a Flow Diagram 11
3.2. Develop an LCI Data Collection Plan 12
3.2.1. Define Data Quality Goals 13
3.2.2. Identify Data Quality Indicators 13
3.2.3. Identify Data Sources and Types 13
3.2.4. Develop a Data Collection Spreadsheet 14
3.3. Collect Data 15
3.3.1. Inputs in the Product Life-Cycle Inventory Analysis 16 Energy 18 Energy Sources 18 Water 19
3.3.2. Outputs of the Product Life-Cycle Inventory Analysis 19 Atmospheric Emissions 20 Waterborne Wastes 20 Solid Waste 21 Products 21 Transportation 22 Co-Product Allocation 22 Industrial Scrap 23 Data-Time Period 23 Specific Data versus Composite Data 23 Geographic Specificity 24
Life Cycle Inventory Analysis (LCIA)
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2 Data Categories 24 Routine/Fugitive/Accidental Releases 24 Special Case Boundary Issues 24
3.3.3. Economic Input-Output Approach to LCIA 25
3.4. Evaluate and Document the LCI Results 25
4. Interpretation of Data 27
5. Case Study – 1: Environmental Impact Minimization Issues 29
in Batch/Semi-Continuous Plants
5.1. A methodology for environmental impact minimization 32
of batch plants
5.1.1. Definition of batch plant system bound (w) 32
5.1.2. Aggregated environmental impact assessment 33
5.1.3. Incorporation of environmental impact criteria 34
in the design and scheduling of batch plants
6. Case Study – 2: Life Cycle Inventory Analysis of 36
Hard Coal Based Electricity Generation
6.1. Inputs and outputs per 1 MWh electricity produced 37
6.2. Inputs and outputs according to the life cycle stages 38
6.3. Waste generation and land use 41
6.4. Sensitivity Analysis 42
6.5. Hard coal transportation distance 42
6.6. Power plant technology 43
6.7 External electricity and heat generation 43
6.8 Use of fly ash in cement manufacturing 43
7. Case Study – 3: Comparison of End-of-Life Tyre 45
Treatment Technologies: Life Cycle Inventory Analysis
7.1. Introduction of the problem 45
7.2. Object of the study 46
7.3. Methodology 46
7.3.1. Functional unit and system boundaries 46
7.3.2. Life cycle inventory analysis 47
7.4. Results and Discussion 47
8. Conclusions 52
References 53
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The development of industrial technology has enabled the transformation of the
environment in different ways, changing the nature and extent of the environmental
impacts of industrial activities. Resource depletion, air, water and land pollution, are
examples of the environmental problems which have emerged as a result of intensified
interventions into the environment. The chemical and process industries find themselves
constantly under the scrutiny of various pressure groups demanding more
environmentally acceptable processes, products and practices through the ideas of `waste
minimization', `zero emission', `producer responsibility', etc. One of the potential dangers
of this is that the companies exposed to environmental pressures may simply respond to
satisfy a particular group. However, this short-term approach may lead to costly long
term mistakes with little environmental improvement and no net business benefit. To
avoid this, environmental issues must be assessed in a holistic way, along side financial,
technical and other criteria (Azapagic, 1999).
A product's life cycle starts when raw materials are extracted from the earth, followed by
manufacturing, transport and use, and ends with waste management including recycling
and final disposal. At every stage of the life cycle there are emissions and consumption of
resources. The environmental impacts from the entire life cycle of products and services
need to be addressed. To do this, life cycle thinking is required.
Life Cycle Assessment (LCA) is a tool for the systematic evaluation of the environmental
aspects of a product or service system through all stages of its life cycle. The assessment
begins with the raw materials input, proceeds through the manufacturing processes,
energy use, maintenance, and transportation. It considers use, reuse, and recycling, and
concludes with waste management, the environmental impact of packaging, and ultimate
disposal of the product (Azapagic, 1996). LCA provides an adequate instrument for
environmental decision support. Life cycle assessment has proven to be a valuable tool to
document the environmental considerations that need to be part of decision-making
towards sustainability. A reliable LCA performance is crucial to achieve a life-cycle
The Fig. 1 shows the interactions of various stages of LCA (Susan, 1995). The key
elements of LCA are given below:
1. Identifies and quantifies the environmental loads involved; e.g. the energy and raw
materials consumed, the emissions and wastes generated (Life Cycle Inventory
2. Evaluates the potential environmental impacts of these loads (Life Cycle Impact
3. Assesses the options available for reducing these environmental impacts
(Interpretation of Life Cycle Inventory analysis and Impact Assessment).
In the present study mainly focus on Life Cycle Inventory analysis (LCIA)
Life Cycle Inventory Analysis (LCIA)
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Fig. 1. Interaction between LCA stages
In the second, Inventory Analysis stage, material and energy balances are performed and
the environmental burdens are quantified. The burdens are defined by resource
consumption and emissions to air, water and solid waste. Aggregation of the burdens into
a smaller number of impact categories (Classification) is done in the Inventory Analysis
stage. In the present study mainly focus on Life Cycle Inventory analysis (LCIA).
Life Cycle Inventory Analysis (LCIA) is a part of the Life Cycle Assessment (LCA), a
thorough procedure accounting for the environmental loads during the product’s life
cycle (Babu and Ramkrishna, 2003). Inventory Analysis is a systematic, objective,
stepwise procedure for quantifying energy and raw materials requirement, atmospheric
emissions, water borne emissions, solid wastes, and other releases for the entire life cycle
of a product, package process, material or activity (Manjare and Babu, 2005). LCIA is a
process of data collection and calculations intended to quantify the inputs and outputs of
a product system. These inputs and outputs may include resources used, as well as release
to air, water, or land (SAIC, 2006).
An inventory may be conducted to aid in decision making by enabling companies or
organizations to:
Develop a baseline for a system’s overall resource requirements for benchmarking
Identify components of the process that are good targets for resource-reduction efforts
Aid in the development of new products or processes that will reduce resource
requirements or emissions.
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Compare alternative materials, products, processes, or activities within the
Compare internal inventory information to that of other manufacturers.
Managers using LCA to aid decision-making can improve the validity of the results and
keep the analysis focused by precisely defining the scope of the “system” to be analyzed,
considering practical constraints such as time and money. This step builds the foundation
for the analysis that follows and should be understood and agreed upon by those
responsible for commissioning the study. A system refers to a collection of operations
that together perform some defined function. The system begins with all the raw
materials taken from the environment and ends with the outputs released back to the
environment as shown in Fig. 2 (Susan, 1995).
Fig.2. Input and output of a system.
Within most systems, three main groups of operations may be defined:
1. Operations for the production, use, transportation, and disposal of the product.
2. Operations for the production of ancillary materials such as packaging
3. The energy production needed to power the system.
A clearly defined scope will improve the results of subsequent steps when the total
process is divided into subsystems. An example of typical subsystem categories is shown
in Fig. 3 (Susan, 1995).
Fig. 3. Defining System Boundaries.
The linkages between subsystems make the process of collecting consistent
measurements complex. For example, subsystems must be defined so that they are large
enough to provide sufficient data for analysis but not so large that data is aggregated at a
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level that precludes detailed analysis. In addition, subsystems should be linked by a
standard basis of comparison such as equivalent usage ratios.
A thorough understanding of how an inventory analysis is conducted, and the limitations
and assumptions inherent in the various stages is critical to effective use of LCA in
decision making. The following is a synopsis of the various subsystems analyzed in an
inventory analysis.
2.1. Raw Materials Acquisition
Data are collected for this subsystem on all activities required to obtain raw materials,
including transportation of the materials to the point of manufacture as shown in Fig. 4
(Susan, 1995).
Fig. 4. Raw material acquisition subsystem.
Typically, raw materials are traced for the primary product and all primary, secondary
and tertiary packaging. Managers should review the data to make sure equivalent
comparisons are used. For example, a package containing recycled materials may need
increased thickness to compensate for the decreased strength of recycled materials. In this
case, managers must make a tradeoff between weight of materials that will someday
become part of the waste stream and virgin material content. The inventory should also
include all inputs of energy, materials, and equipment necessary for acquiring each raw
material. Because this dramatically increases the complexity of the analysis, criteria must
be determined to eliminate insignificant contributions. This may be done by establishing
a threshold for inclusion. For example, any component contributing less than five percent
of inputs might be ignored.
Ecosystems are impacted in many ways by the extraction or harvesting of raw materials,
but only those effects that can be quantified, such as pesticide run-off from agriculture or
soil loss from logging, should be included in the inventory. Effects that cannot be easily
measured, such as loss of scenic or aesthetic value, may be covered in the more
subjective impact assessment. At this point, attempts to quantify renewable or
nonrenewable resources for inventory calculations are subjective, as quantifiable data is
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not publicly available. However, maintaining separate lists of renewable and
nonrenewable materials may be helpful if an impact assessment is later performed.
Energy acquisition is actually part of the materials-acquisition subsystem, but because of
the complexity of the subject, it warrants its own analysis. Data collected should include
all energy requirements and emissions attributed to the acquisition, transportation, and
processing of fuels. This means that if gasoline is used as a transportation fuel, not only
should emissions related to combustion be included, but also energy consumption and
emissions due to extraction and refining. In the U.S., energy is derived from a number of
sources including coal, natural gas, petroleum, hydropower, nuclear power, and wood.
Utilities use many different types of energy sources to produce electricity, so the energy
analysis must include a determination of the fuel mix used to generate the electricity.
Generally, the national average fuel mix may be used, but industry-specific information is
Some materials are made from energy resources and are therefore assigned an energy
value. For example, plastics, made from petroleum and natural gas, release energy when
burned. This energy value is credited against the system requirements for the primary
product, resulting in a new energy requirement that is less than the total energy
requirements for the system.
2.2. Manufacture and Fabrication
Data collected for this subsystem includes all energy, material, or water inputs and
environmental releases that occur during the manufacturing processes required to convert
each raw material input into intermediate materials ready for fabrication. This process
may be repeated for several streams of resources as well as several intermediate cycles
before final fabrication of the product as shown in Fig. 5 (Susan, 1995).
Fig. 5. Manufacturing and fabrication system.
Often co-products – outputs that are neither products nor inputs elsewhere in the system –
are generated in the manufacturing process. Co-products are included in LCA until they
are separated from the primary product being analyzed. Raw materials, energy, and
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emissions should be allocated between the primary product and the co-products by their
proportionate weight or volume. If scrap within one subsystem is used as an input within
the same subsystem, the raw material or intermediate material required from the outside
is reduced and should be factored into the analysis. If industrial scrap is used in another
subsystem, it is considered to be a co-product and should be allocated to the same
consumption and emission rates required to produce the primary material. Some scrap is
simply discarded and should be counted as solid waste.
Differences in technology throughout the industry require certain assumptions to be made
at this stage. Comparisons between different-size facilities, differing ages of equipment,
different capacity-utilization rates, and differing energy consumption per unit of
production must be made explicit.
The data collected for final product fabrication assesses the consumption of inputs and
the emissions required to convert all materials into the final product ready for consumer
purchase. Calculations follow the same procedure as in converting raw material to
intermediate materials and include the same limitations.
Data collected for fabrication of the final product includes the inputs and releases
associated with filling and packaging operations. As this is a necessary step for virtually
any product, this step focuses on differences between processes or materials being
compared. If the filling procedure is identical for the two products being compared, this
step can be ignored. Both primary and secondary packaging must be included in the
calculations, taking care to keep packaging per unit consistent between alternatives.
2.3. Transportation/Distribution
An inventory of the related transportation activities of the product to warehouses and
end-users may be simplified by using standards for the average distance transported and
the typical mode of transportation used as shown in Fig. 6 (Susan, 1995).
Fig. 6. Transportation/Distribution system.
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Inventory of the distribution process includes warehousing, inventory control, and
repackaging. Environmental controls such as refrigeration are components of both
transportation and distribution. As in previous stages, clear boundaries must be
established to define the extent to which issues such as building and maintaining
transportation and distribution equipment will be factored into the inventory results.
2.4. Consumer Use/Disposal
Data collected for this subsystem cover consumer activities including use (product
consumption, storage, preparation, or operation), maintenance (repair), and reuse is
shown in Fig. 7.
Fig. 7. Consumer use/disposal system.
Issues to consider when defining the scope of the subsystem include:
Time of product use before it is discarded.
Inputs used in the maintenance process.
The typical frequency of repair.
Potential product reuses options.
Managers should incorporate into the analysis any industry information on typical
consumer usage patterns that may make the study’s results more valid. For example,
consumers may occasionally use two thinner paper cups to attain the strength of a single
comparable polystyrene cup. Sources of data that may help this process include consumer
surveys, published materials, and assumptions. Inventory reports must include
documentation of assumptions including the timeliness of the data, potential biases, and
other limitations.
Various disposal alternatives exist such as reuse, recycling, composting, incineration, and
landfilling. Transportation and collection of post-consumer waste should also be included
in the analysis. Inventories often use a national estimate of waste management methods,
citing current averages for the percentage of waste disposed of by landfilling, recycling,
and incineration methods.
2.5. Recycling
Recycling technology is expected to improve greatly in the future. Therefore, content
levels and recycling rates should always be reported at current rates with documentation
of study dates. Advances in technology will both increase rates and the number of
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products that are recyclable, altering both open and closed-loop recycling options as
shown in Fig. 7 (Susan, 1995).
Fig. 8. Recycling subsystem
Open-loop recycling means that a product is recycled into a different product that is
disposed of after use. In these cases, the resource requirements and environmental
emissions related to the recycling and final disposal of the recycled material is divided
equally between the two products produced.
Closed-loop recycling refers to materials that can be recycled into the same product
repeatedly. This means that the more times the product is recycled, the less virgin
material is required and the greater the number of cycles over which the resources and
emissions can be allocated. The environmental effects of a closed-loop product will
approach zero over the life of the product. For some products, a recycling infrastructure
already exists, providing data on the collection, transportation, and processing of its
materials. But for many products such information does not exist, leading to the use of
data extrapolated from pilot programs or forecasts.
Wastes may be defined as materials that have no intrinsic or market value. Waste occurs
in some form at every stage of the life cycle. Careful analysis of waste management
issues is required as disposal options vary with the seasons, geography, and the
technology used by a particular facility. Further complicating the inventory is the fact that
many waste streams are combinations of materials derived from several subsystems, and
that waste treatment facilities may produce a variety of releases including air, water, and
solid wastes. For example, reported waterborne waste data should include an analysis of
the water treatment system, the land associated with the treatment system, and
atmospheric and solid wastes associated with the system. Information about emissions
from solid waste is more difficult to find as there is no existing method to determine the
emissions of a particular product once it has been mixed with municipal waste in a
landfill or incinerator. If, however, a disposal process is being used for only one type of
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product (e.g., composting for yard waste or recycling for aluminum cans), accurate
measures are available.
EPA’s 1993 document, “Life-Cycle Assessment: Inventory Guidelines and Principles,”
and 1995 document, “Guidelines for Assessing the Quality of Life Cycle Inventory
Analysis,” provide the framework for performing an inventory analysis and assessing the
quality of the data used and the results. The two documents define the following four
steps of a life cycle inventory:
1. Develop a flow diagram of the processes being evaluated.
2. Develop a data collection plan.
3. Collect data.
4. Evaluate and report results.
3.1. Develop a Flow Diagram
A flow diagram is a tool to map the inputs and outputs to a process or system. The
“system” or “system boundary” varies for every LCA project. The goal definition and
scoping phase establishes initial boundaries that define what is to be included in a
particular LCA; these are used as the system boundary for the flow diagram. Unit
processes inside of the system boundary link together to form a complete life cycle
picture of the required inputs and outputs (material and energy) to the system. Fig. 9
illustrates the components of a generic unit process within a flow diagram for a given
system boundary (SAIC, 2006).
Fig. 9. Generic unit process.
The more complex the flow diagram, the greater the accuracy and utility of the results.
Unfortunately, increased complexity also means more time and resources must be
devoted to this step, as well as the data collecting and analyzing steps.
Flow diagrams are used to model all alternatives under consideration (e.g., both a
baseline system and alternative systems). For a comparative study, it is important that
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both the baseline and alternatives use the same system boundary and are modeled to the
same level of detail. If not, the accuracy of the results may be skewed.
For data-gathering purposes it is appropriate to view the system as a series of subsystems.
A “subsystem” is defined as an individual step or process that is part of the defined
production system. Some steps in the system may need to be grouped into a subsystem
due to lack of specific data for the individual steps. For example, several steps may be
required in the production of bar soap from tallow. However, these steps may all occur
within the same facility, which may not be able to or need to break data down for each
individual step. The facility could however, provide data for all the steps together, so the
subsystem boundary would be drawn around the group of soap production steps and not
around each individual one.
Each subsystem requires inputs of materials and energy; requires transportation of
product produced; and has outputs of products, co-products, atmospheric emissions,
waterborne wastes, solid wastes, and possibly other releases. For each subsystem, the
inventory analyst should describe materials and energy sources used and the types of
environmental releases. The actual activities that occur should also be described. Data
should be gathered for the amounts and kinds of material inputs and the types and
quantities of energy inputs. The environmental releases to air, water, and land should be
quantified by type of pollutant. Data collected for an inventory should always be
associated with a quality measure. Although formal data quality indicators (DQIs) such
as accuracy, precision, representativeness, and completeness are strongly preferred, a
description of how the data were generated can be useful in judging quality.
Co-products from the process should be identified and quantified. Co-products are
process outputs that have value, i.e., those not treated as wastes. The value assigned to a
co-product may be a market value (price) or may be imputed. In performing co-product
allocation, some means must be found to objectively assign the resource use, energy
consumption, and emissions among the co-products, because there is not a physical or
chemical way to separate the activities that produce them. Generally, allocation should
allow technically sound inventories to be prepared for products or materials using any
particular output of a process independently and without overlap of the other outputs.
3.2. Develop an LCI Data Collection Plan
As part of the goal definition and scoping phase, the required accuracy of data was
determined. When selecting sources for data to complete the life cycle inventory, an LCI
data collection plan ensures that the quality and accuracy of data meet the expectations of
the decision-makers. Key elements of a data collection plan include the following:
1. Defining data quality goals
2. Identifying data quality indicators
3. Identifying data sources and types
4. Developing a data collection worksheet and checklist.
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3.2.1. Define Data Quality Goals
Data quality goals provide a framework for balancing available time and resources
against the quality of the data required to make a decision regarding overall
environmental or human health impact (EPA, 1986). Data quality goals are closely linked
to overall study goals and serve two primary purposes:
1. Aid LCA practitioners in structuring an approach to data collection based on the data
quality needed for the analysis.
2. Serve as data quality performance criteria.
No pre-defined list of data quality goals exists for all LCA projects. The number and
nature of data quality goals necessary depends on the level of accuracy required to inform
the decision-makers involved in the process.
3.2.2. Identify Data Quality Indicators
Data quality indicators are benchmarks to which the collected data can be measured to
determine if data quality requirements have been met. Similar to data quality goals, there
is no pre-defined list of data quality indicators for all LCIs. The selection of data quality
indicators depends upon which ones are most appropriate and applicable to the specific
data sources being evaluated. Examples of data quality indicators are precision,
completeness, representativeness, consistency, and reproducibility.
3.2.3. Identify Data Sources and Types
For each life cycle stage, unit process, or type of environmental release, specify the
necessary data source and/or type required to provide sufficient accuracy and quality to
meet the study’s goals. Defining the required data sources and types prior to data
collection helps to reduce costs and the time required to collect the data. The required
level of aggregated data should also be specified, for example, whether data are
representative of one process or several processes.
A number of sources should be used in collecting data. Whenever possible, it is best to
get well-characterized industry data for production processes. Manufacturing processes
often become more efficient or change over time, so it is important to seek current data.
Inventory data can be facility-specific or more general and still remain current.
Several categories of data are often used in inventories. Starting with the most
disaggregated, these are:
Individual process and facility-specific: data from a particular operation within a given
facility that are not combined in any way.
Composite: data from the same operation or activity combined across locations.
Aggregated: data combining more than one process operation.
Industry-average: data derived from a representative sample of locations and believed to
statistically describe the typical operation across technologies.
Generic: data whose representativeness may be unknown but which are qualitatively
descriptive of a process or technology.
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3.2.4. Develop a Data Collection Spreadsheet
The next step is to develop a life cycle inventory spreadsheet that covers most of the
decision areas in the performance of an inventory. A spreadsheet can be prepared to
guide data collection and validation and to enable construction of a database to store
collected data electronically. The following eight general decision areas should be
addressed in the inventory spreadsheet:
1. Purpose of the inventory
2. System boundaries
3. Geographic scope
4. Types of data used
5. Data collection procedures
6. Data quality measures
7. Computational spreadsheet construction
8. Presentation of results.
The spreadsheet is a valuable tool for ensuring completeness, accuracy, and consistency.
It is especially important for large projects when several people collect data from
multiple sources. The spreadsheet should be tailored to meet the needs of a specific LCI.
The overall system flow diagram, derived in the previous step, is important in
constructing the computational spreadsheets because it numerically defines the
relationships of the individual subsystems to each other in the production of the final
product. These numerical relationships become the source of “proportionality factors,”
which are quantitative relationships that reflect the relative contributions of the
subsystems to the total system.
It is important that each subsystem be incorporated in the spreadsheet with its related
components and that each be linked together in such as way that inadvertent omissions
and double-counting do not occur. The spreadsheet can be organized in several different
ways to accomplish this purpose. These can include allocating certain fields or areas in
the spreadsheet to certain types of calculations or using one type of spreadsheet software
to actually link separate spreadsheets in hierarchical fashion. It is imperative, however,
once a system of organization is used, that it be employed consistently. Haphazard
organization of data sets and calculations generally leads to faulty inventory results.
Many decisions must be made in every life-cycle inventory analysis. Every inventory
consists of a mix of factual data and assumptions. Assumptions allow the analyst to
evaluate a system condition when factual data either cannot be obtained within the
context of the study or do not exist. Each piece of information (e.g., the weight of
paperboard used to package the soap, type of vehicle and distance for shipping the tallow,
losses incurred when rendering tallow, or emissions resulting from the animals at the
feedlot), fall into one or the other category and each plays a role in developing the overall
system analysis. Because assumptions can substantially affect study results, a series of
“what if” calculations or sensitivity analyses are often performed on the results to
examine the effect of making changes in the system. A sensitivity analysis will
temporarily modify one or more parameters and affect the calculation of the results.
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Observing the change in the results will help determine how important the assumptions
are with respect to the results. The computational spreadsheet is also used to perform
these sensitivity analysis calculations.
Sometimes it is helpful to think ahead about how the results will be presented. This can
direct some decisions on how the spreadsheet output is specified. The analyst must
remember the defined purpose for performing the analysis and tailor the data output to
those expressed needs.
3.3. Collect Data
Data collection efforts involve a combination of research, site-visits and direct contact
with experts, which generates large quantities of data. As an alternative, it may be more
cost effective to buy a commercially available LCA software package. Prior to
purchasing an LCA software package the decision-makers or LCA practitioner should
insure that it will provide the level of data analysis required.
A second method to reduce data collection time and resources is to obtain non-site
specific inventory data. Several organizations have developed databases specifically for
LCA that contain some of the basic data commonly needed in constructing a life cycle
inventory. Some of the databases are sold in conjunction with LCI data collection
software; others are stand-alone resources. Many companies with proprietary software
also offer consulting services for LCA design. The use of commercial software risks
losing transparency in the data. Often there is no record of assumptions or computational
methods that were used. This may not be appropriate if the results are to be used in the
public domain. Revisiting the goal statement is needed in order to determine if such data
are appropriate.
All industrial processes have multiple input streams and many generate multiple output
streams. Usually only one of the outputs is of interest for the life cycle assessment study
being conducted, so the analyst needs to determine how much of the energy and material
requirements and the environmental releases associated with the process should be
attributed, or allocated, to the production of each co-product. For example, steam turbine
systems may sell both electricity and low-pressure steam as useful products. When co-
products are present, the practitioner must determine how much of the burdens associated
with operating and supplying the multi-output process should be allocated to each co-
product. The practitioner must also decide how to allocate environmental burdens across
co-products when one is a waste stream that can be sold for other uses.
The guidance provided by the International Standards Organization (ISO) recognizes the
variety of approaches that can be used to treat the allocation issue and, therefore, requires
a step-wise approach. The following stepwise procedure shall be applied.
Step 1: Wherever possible, allocation should be avoided by:
Dividing the unit process to be allocated into two or more sub-processes and
collecting the input and output data related to these sub-processes.
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Expanding the product system to include the additional functions related to the
co-products, taking into account the requirements of (function, functional unit,
and reference flow).
Step 2: Where allocation cannot be avoided, the inputs and outputs of the system should
be partitioned between its different products or functions in a way which reflects the
underlying physical relationships between them, i.e., they shall reflect the way in which
the inputs and outputs are changed by quantitative changes in the products or functions
delivered by the system. The resulting allocation will not necessarily be in proportion to
any simple measurement such as mass or molar flows of co-products.
Step 3: Where physical relationship alone cannot be established or used as the basis for
allocation, the inputs should be allocated between the products and functions in a way
which reflects other relationships between them. For example, input and output data
might be allocated between co-products in proportion to the economic value of the
products. The flow diagram(s) developed in Step-1 provides the road map for data to be
Step 2 specifies the required data sources, types, quality, accuracy, and collection
methods. Step 3 consists of finding and filling in the flow diagram and worksheets with
numerical data. This may not be a simple task. Some data may be difficult or impossible
to obtain, and the available data may be difficult to convert to the functional unit needed.
Therefore, the system boundaries or data quality goals of the study may have to be
refined based on data availability. This iterative process is common for most LCAs.
3.3.1. Inputs in the Product Life-Cycle Inventory Analysis
The decision on which raw/intermediate material requirements to include in a life-cycle
inventory is complex, but several options are available:
Incorporate all requirements, no matter how minor, on the assumption that it is not
possible a priori to decide to exclude anything.
Within the defined scope of the study, exclude inputs of less than a predetermined
and clearly stated threshold.
Within the defined scope of the study, exclude inputs determined likely to be
negligible, relative to the intended use of the information, on the basis of a sensitivity
Within the defined scope, consistently exclude certain classes or types of inputs, such
as capital equipment replacement.
The advantage of the first option is that no assumptions are made in defining and drawing
the system boundary. The analyst does not have to explain or defend what has been
included or excluded. The disadvantage is that application of this approach could be an
endless exercise. The number of inputs could be very large and could include some
systems only distantly related to the product system of interest. Besides the
computational complexity, interpretation of the results with respect to the single desired
product, package, or activity could be difficult.
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The second option, if implemented with full explanation of what the threshold is and why
it was selected, would have the advantages of consistency and lower cost and time
investments. Two sub-options can be identified, depending on the nature of the threshold.
One sub-option is to specify a percentage contribution below which the material will be
excluded, for example, one percent of the input to a given subsystem or to the entire
system. The one percent rule historically has been useful in limiting the extent of the
analysis in inventories where the environmental consequences of quantitatively minor
materials are not considered. The disadvantage of the one percent rule is that the possible
presence of an environmentally damaging activity associated with these materials could
be overlooked. Also, when used with mixed percentages (e.g., percent of system energy,
percent of subsystem input), the result may be confusing or inconsistent. The scoping
analysis should provide a rationale for choosing to apply such a rule.
The second sub-option is to set a threshold based on the number of steps that the
raw/intermediate material is removed from the main process sequence. Caustic
manufacture from brine electrolysis is part of the main process sequence and would
clearly be included. Sodium carbonate is an input material for the production of caustic is
therefore a secondary input. Applying a “one-step back” decision rule would include the
steps associated with sodium carbonate production. Ammonium chloride is an input
material for the production of sodium carbonate using the Solvay process. Relative to
caustic, ammonium chloride is a tertiary input and would be excluded if a “one-step
back” decision rule were applied. As in the first option, the “one-step back” decision rule
has the advantages of clarity and consistent application. For some inputs that are
analyzable in exact mathematical terms, the “one-step back” rule may be justifiable. If
the inputs to a given process bear a fixed relationship to the next-tier process, one step is
all that may be necessary to obtain a sufficiently accurate value (Boustead and Hancock
The third option, drawing boundaries based on sensitivity analysis, adds the advantage of
being systematic rather than arbitrary in assigning the threshold. The disadvantages of a
sensitivity analysis-based approach are that the analyst needs to be very clear in
describing how the analysis was used and, unless a large existing database is available to
supply preliminary values that can be used in the sensitivity analysis, the required
analysis effort may not be limited by a very large amount.
The final option, excluding certain classes or types of input, also has been found through
experience to apply to many systems. The advantage of this option is that many complex
subsystems can often be excluded. The disadvantages are the same as those for the first
option, namely, that a highly significant activity may be eliminated. Capital equipment is
the most commonly excluded input type. The analyst should perform a preliminary
analysis to characterize the basic activities in each class or type of input to ensure that a
significant contribution is not left out.
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18 Energy
Energy represents a combination of energy requirements for the subsystem. Three
categories of energy are quantifiable: process, transportation, and energy of material
resources (inherent energy).
Process energy is the energy required to operate and run the subsystem process(es),
including such items as reactors, heat exchangers, stirrers, pumps, blowers, and boilers.
Transportation energy is the energy required to power various modes of transportation
such as trucks, rail carriers, barges, ocean vessels, and pipelines. Conveyors, forklifts,
and other equipment that could be considered with transportation or process are labeled
according to their role in the subsystem. For example, power supplied to a conveyor used
to carry material from one point in the subsystem would be labeled process energy. On
the other hand, the power supplied to a conveyor used to transport material from one
subsystem to a different subsystem would be considered transportation energy.
Two alternatives exist for incorporating energy inputs in a subsystem module. One is to
report the actual energy forms of the inputs, e.g., kilowatt-hours (kWh) of electricity or
cubic feet of natural gas. The other is to include the specific quantities of fuels used to
generate the produced energy forms in the module.
The advantage of the first approach is that the specific energy mix is available for each
subsystem. For example, a company may want to evaluate the desirability of installing a
natural gas-fired boiler to produce steam compared to using its electrically heated boiler
powered by a combination of purchased and on-site generated electricity. A specific fuel
mix could be applied to compute the energy and fuel resource use. The second approach,
incorporating specific fuel quantities, allows a subsystem comparison of primary energy
Within each subsystem, the energy input data should be given as specific quantities of
fuel and then converted into energy equivalents according to the conversion factors. Energy Sources
Energy is obtained from a variety of sources, including coal, nuclear power, hydropower,
natural gas, petroleum, wind, solar energy, solid waste, and wood biomass. Fuels are
interchangeable, to a high degree, based on their energy content. For example, an electric
utility decides which fuel or other energy source to use based on the cost per energy unit.
Utilities can and do use multiple forms of energy sources, making possible an economic
decision based on the energy cost per kilowatt-hour of electricity generated.
Manufacturing companies also choose among energy sources on the same basis.
However, reasons other than cost, such as scarcity or emissions to the environment, also
affect the energy source decision. For example, during periods of petroleum shortages,
finding products that use predominantly non-petroleum energy sources may be desirable.
For that reason, the inventory should characterize energy requirements according to basic
sources of energy. Thus, it would consider not only electricity, but also the basic sources
(such as coal, nuclear power, hydropower, natural gas, and petroleum) that produce the
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Water volume requirements should be included in a life-cycle inventory analysis. In some
locations, water is plentiful. Along the coasts, seawater is usable for cooling or other
manufacturing purposes. However, in other places water is in short supply and must be
allocated for specific uses. Some areas have abundant water in some years and limited
supplies in other years. Some industrial applications reuse water with little new or
makeup water required. In other applications, however, tremendous amounts of new
water inputs are required.
How should water be incorporated in an inventory?
The goal of the inventory is to measure, per unit of product, the gallons of water required
that represent water unavailable for beneficial uses (such as navigation, aquatic habitat,
and drinking water). Water withdrawn from a stream, used in a process, treated, and
replaced in essentially the same quality and in the same location should not be included
in the water-use inventory data. Ideally, water withdrawn from groundwater and
subsequently discharged to a surface water body should be included, because the
groundwater is not replaced to maintain its beneficial purposes. Data to make this
distinction may be difficult to obtain in a generic study where site-specific information is
not available.
In practice, the water quantity to be estimated is net consumptive usage. Consumptive
usage as a life-cycle inventory input is the fraction of total water withdrawal from surface
or groundwater sources that either is incorporated into the product, co-products (if any),
or wastes, or is evaporated. As in the general case of renewable versus nonrenewable
resources, valuation of the degree to which the water is or is not replenishable is best left
to the impact assessment.
3.3.2. Outputs of the Product Life-Cycle Inventory Analysis
A traditional inventory qualifies three categories of environmental releases or emissions:
atmospheric emissions, waterborne waste, and solid waste. Products and co-products also
are quantified. Most inventories consider environmental releases to be actual discharges
(after control devices) of pollutants or other materials from a process or operation under
evaluation. Inventory practice historically has included only regulated emissions for each
process because of data availability limitations. It is recommended that analysts collect
and report all available data in the detailed tabulation of subsystem outputs. In a study not
intended for product comparisons, all of these pollutants should be included in the
summary presentations.
A comparative study offers two options. The first is to include in the summary
presentation only data available for alternatives under consideration. The advantage of
this option is that it gives a comparable presentation of the loadings from all the
alternatives. The disadvantage is that potentially consequential information, which is
available only for some of the alternatives, may not be used. The second option is to
report all data whether uniformly available or not. In using this option, the analyst should
caution the user not to draw any conclusions about relative effects for pollutants where
comparable data are not available. “Comparable” is used here to mean the same pollutant.
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For example, in a summary of data on a bleached paper versus plastic packaging
alternatives, data on dioxin emissions may be available only for the paper product. The
second option is recommended for internal studies and for external studies where proper
context can be provided. Atmospheric Emissions
Atmospheric emissions are reported in units of weight and include all substance classified
as pollutants per unit weight of product output. These emissions generally have included
only those substances required by regulatory agencies to be monitored but should be
expanded where feasible. The amounts reported represent actual discharges into the
atmosphere after passing through existing emission control devices. Some emissions,
such as fugitive emissions from valves or storage areas, may not pass through control
devices before release to the environment. Atmospheric emissions from the production
and combustion of fuel for process or transportation energy (fuel-related emissions), as
well as the process emissions, are included in the life-cycle inventory.
Typical atmospheric emissions are particulates, nitrogen oxides, volatile organic
compounds (VOCs), sulfur oxides, carbon monoxide, aldehydes, ammonia, and lead.
This list is neither all-inclusive nor is it a standard listing of which emissions should be
included in the life-cycle inventory. Recommended practice is to obtain and report
emissions data in the most speciated form possible. Some air emissions, such as
particulates and VOCs, are composites of multiple materials whose specific makeup can
vary from process to process. All emissions for which there are obtainable data should be
included in the inventory. Therefore, the specific emissions reported for any system,
subsystem, or process will vary depending on the range of regulated and non-regulated
Certain materials, such as carbon dioxide and water vapor losses due to evaporation
(neither of which is a regulated atmospheric emission for most processes), have not been
included in most inventory studies in the past. Regulations for carbon dioxide are
changing as the debate surrounding the greenhouse effect and global climate change
continues and the models used for its prediction are modified. Inclusion of these
emerging emissions of concern is recommended. Waterborne Wastes
Waterborne wastes are reported in units of weight and include all substances generally
regarded as pollutants per unit of product output. These wastes typically have included
only those items required by regulatory agencies, but the list should be expanded as data
are available. The effluent values include those amounts still present in the waste stream
after wastewater treatment, and represent actual discharges into receiving waters. For
some releases, such as spills directly into receiving waters, treatment devices do not play
a role in what is reported. For some materials, such as brine water extracted with crude
oil and reinjected into the formation, current U.S. regulations do not define such materials
as waterborne wastes, although they may be considered in solid waste regulations under
the Resource Conservation and Recovery Act (RCRA). Other liquid wastes may also be
deep well injected and should be included. In general, the broader definition of emissions
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in a life-cycle inventory, in contrast to regulations, would favor inclusion of such streams.
It can be argued, from a systems analysis standpoint, that materials such as brine should
count as releases from the subsystem because they cross the subsystem boundary. If
wastes and spills that occur are discharged to the ocean or some other body of water,
these values are always reported as wastes.
As with atmospheric wastes, waterborne wastes from the production and combustion of
fuels (fuel-related emissions), as well as process emissions, are included in the life-cycle
Some of the most commonly reported waterborne wastes are biological oxygen demand
(BOD), chemical oxygen demand (COD), suspended solids, dissolved solids, oil and
grease, sulfides, iron, chromium, tin, metal ions, cyanide, fluorides, phenol, phosphates,
and ammonia. Again, this listing of emissions is not meant to be a standard for what
should be included in an inventory. Some waterborne wastes, such as BOD and COD,
consist of multiple materials whose composition can vary from process to process. Actual
waterborne wastes will vary for each system depending on the range of regulated and
non-regulated chemicals. Solid Waste
Solid waste includes all solid material that is disposed from all sources within the system.
Solid wastes typically are reported by weight. A distinction is made between industrial
solid wastes and post-consumer solid wastes, as they are generally disposed of in
different ways and, in some cases, at different facilities. Industrial solid waste refers to
the solid waste generated during the production of a product and its Post-consumer solid
waste refers to the product/packaging once it has met its intended use and is discarded
into the municipal solid waste stream.
Process solid waste is the waste generated in the actual process, such as trim or waste
materials that are not recycled, as well as sludges and solids from emissions control
devices. Fuel-related waste is solid waste produced from the production and combustion
of fuels for transportation and operating the process. Fuel combustion residues, mineral
extraction wastes, and solids from utility air control devices are examples of fuel-related
wastes. Products
The products are defined by the subsystem and/or system under evaluation. In other
words, each subsystem will have a resulting product, with respect to the entire system.
This subsystem product may be considered either a raw material or intermediate material
with respect to another system, or the finished product of the system. All other material
outputs (not released as wastes or emissions) are considered co-products. Classifying a
material as a product in a life-cycle study depends, in part, on the extent of the system
being examined, i.e., the position from which the material is viewed or the analyst’s point
of view.
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22 Transportation
The life-cycle inventory includes the energy requirements and emissions generated by the
transportation requirements among subsystems for both distribution and disposal of
wastes. Transportation data are reported in miles or kilometers shipped. This distance is
then converted into units of ton-miles or ton-kilometers, which is an expression involving
the weight of the shipment and the distance shipped. Materials typically are transported
by rail, truck, barge, pipeline, and ocean transport. The efficiency of each mode of
transport is used to convert the units of ton-miles into fuel units (e.g., gallons of diesel
fuel). The fuel units are then converted to energy units, and calculations are made to
determine the emissions generated from the combustion of the fuels. Co-Product Allocation
Most industrial processes are physical and/or chemical processes. The fundamentals of
life-cycle inventory are based on modeling a system in such a way that calculated values
reasonably represent actual (measurable) occurrences. Some processes generate multiple
output streams in addition to waste streams. In attributional LCAs, only certain of these
output streams are of interest with respect to the primary product being evaluated. The
term co-product is used to define all output streams other than the primary product that
are not waste streams and that are not used as raw materials elsewhere in the system
examined in the inventory. Co-products are of interest only to the point where they no
longer affect the primary product, i.e. the product that is part of the life cycle system
being studied. Subsequent refining of co-products is beyond the scope of the analysis, as
is transport of co-products to facilities for further refining. A basis for co-product
allocation needs to be selected with careful attention paid to the specific items calculated.
Each industrial system must be handled on a case-by-case basis since no allocation basis
exists that is always applicable. In effect, the boundary for the analysis is drawn between
the primary product and co-products, with all materials and environmental loadings
attributed to co-products being outside the scope of the analysis. The first step is to
investigate any complex process in detail and attempt to identify unit sub-processes that
produce the product of interest. If sufficient detail can be found, no co-product allocation
will be necessary.
If a process produces several different chemical products, care must be taken in the
analysis. It will be necessary to write balanced chemical equations and trace the chemical
stoichiometry from the raw materials into the products. A simple mass allocation method
frequently gives reasonable results, but not always. In calculating energy, heat of reaction
may be the appropriate basis for allocating energy to the various co-products.
For environmental emissions from a multiple-product process, allocation to different co-
products may not be possible. It has been suggested that the selling price of the co-
products could be used as a basis for this allocation. Because the selling prices of the
various co-products can vary greatly with time and with independent competitive markets
for each co-product, a market-based approach would have to accommodate such
variations, by using an average value ranged over several years, or similar method.
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One important role of an inventory is to provide information upon which impact
assessment and improvement analysis can be based. In cases where there is no clear
methodological solution, the inventory should include reasonable alternative calculations
or apply sensitivity analysis to determine the effect of allocation on the final results. It
remains at some later time to make the judgments as to which of several reasonable
alternatives is the correct one. In any event, it should be made clear what assumptions
were made and what procedures were used. Industrial Scrap
One co-product stream of particular interest is industrial scrap. This term is used to
specifically identify process wastes of value (trim scraps and off-spec materials) that are
produced as an integral part of a manufacturing process. Further, the wastes have been
collected and used as input materials for additional manufacturing processes. The last
criterion is that these scrap materials have never been used as originally intended when
manufactured. For example, a common polyurethane foam product is seat cushions for
automobiles. The trim from cutting the cushions is never incorporated into seat cushions.
Likewise, off-spec seat cushions sold as industrial scrap are never used as seat cushions,
but are used as input material for another process.
A careful distinction must be made between industrial scrap and post-consumer waste for
proper allocation in the inventory. If the industrial scrap is to be collected and used as a
material input to a production system or process, it is credited in the life-cycle inventory
as a co-product at the point where it was produced. Unfortunately, systems that use
material more efficiently, i.e., that produce lesser amounts of salable co-products, assume
a higher percentage of the upstream energy and releases using the criterion.
When the consumption of a co-product falls within the boundaries of the analysis, it must
no longer be considered as a co-product, but as a primary product carrying with it all the
energy requirements and environmental releases involved with producing it, beginning
with raw materials acquisition. Data Time Period
The time period that data represents should be long enough to smooth out any deviations
or variations in the normal operations of a facility. These variations might include plant
shutdowns for routine maintenance, startup activities, and fluctuation in levels of
production. Often data are available for a fiscal year of production, which is usually a
sufficient time period to cover such variations. Specific Data versus Composite Data
When the purpose of the inventory is to find ways to improve internal operations, it is
best to use data specific to the system that is being examined. These types of data are
usually the most accurate and also the most helpful in analyzing potential improvements
to the environmental profile of a system. However, private data typically are guarded by a
confidentiality agreement, and must be protected from public use by some means.
Composite, industry-average data are preferable when the inventory results are to be used
for broad application across the industry, particularly in studies performed for public use.
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Although composite data may be less specific to a particular company, they are generally
more representative of an industry as a whole. Such composite data can also be made
publicly available, are more widely usable, and are more general in nature. Composite
data can be generated from facility-specific data in a systematic fashion and validated
using a peer review process. Variability, representativeness, and other data quality
indicators can still be specified for composite data. Geographic Specificity
Natural resource and environmental consequences occur at specific sites, but there are
broader implications. It is important to define the scope of interest (regional vs. national
vs. international) in an inventory. A local community may be more interested in direct
consequences to itself than in global concerns. Data Categories
Environmental emission databases usually cover only those items or pollutants required
by regulatory agencies to be reported. For example, as previously mentioned, the
question of whether to report only regulated emissions or all emissions is complicated by
the difficulty in obtaining data for unregulated emissions. In some cases, emissions that
are suspected health hazards may not be required to be reported by a regulatory agency
because the process of adding them to the list is slow. A specific example of an
unregulated emission is carbon dioxide, which is a greenhouse gas suspected as a primary
agent in global warming. There is no current requirement for reporting carbon dioxide
emissions, and it is difficult to obtain measured data on the amounts released from
various processes. Thus, results for emissions reported in a life-cycle inventory may not
be viewed as comprehensive, but they can cover a wide range of pollutants. As a rule, it
is recommended that data be obtained on as broad a range as possible. Calculated or
qualitative information, although less desirable and less consistent with the quantitative
nature of an inventory, may still be useful. Routine/Fugitive/Accidental Releases
Whenever possible, routine, fugitive, and accidental emissions data should be considered
in developing data for a subsystem. If data on fugitive and accidental emissions are not
available, and quantitative estimates cannot be obtained, this deficiency should be noted
in the report on the inventory results. Often estimates can be made for accidental
emissions based on historical data pertaining to frequency and concentrations of
accidental emissions experienced at a facility. Special Case Boundary Issues
In all studies, boundary conditions limiting the scope must be established. The areas of
capital equipment, personnel issues, and improper waste disposal typically are not
included in inventory studies, because they have been shown to have little effect on the
results. Earlier studies did consider them in the analysis; later studies have verified their
minimal contribution to the total system profile. Thus, exclusion of contributions from
capital equipment manufacture, for example, is not excluded a priori. The decision to
include or not to include them should be clearly noted by the analyst.
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3.3.3. Economic Input-Output Approach to LCIA
Economic Input/Output offers an alternative way to create life cycle inventory. The
input/output model divides an entire economy into distinct sectors and represents them in
table, or matrix, form so that each sector is represented by one row and one column. The
matrix represents sales from one sector to another. The economic input-output model is
linear so that the effects of purchasing $1,000 from one sector will be ten times greater
than the effects of purchasing $100 from that sector.
In order to create life cycle inventory, the economic output for each sector is first
calculated, then the environmental outputs are calculated by multiplying the economic
output at each stage by the environmental impact per dollar of output. The advantage of
the economic input/output approach is that it quickly covers an entire economy, including
all the material and energy inputs, thereby simplifying the inventory creation process. Its
main disadvantage is that the data are created at high aggregate levels for an entire
industry, such as steel mills, rather than particular products, such as the type of steel used
to make automobiles.
“Hybrid” models which combine the economic input/output model with process models
have also been proposed in order to utilize the advantages offered by both approaches
(Hendrickson et al., 2006).
3.4. Evaluate and Document the LCI Results
When writing a report to present the final results of the life-cycle inventory, it is
important to thoroughly describe the methodology used in the analysis. The report should
explicitly define the systems analyzed and the boundaries that were set. All assumptions
made in performing the inventory should be clearly explained. The basis for comparison
among systems should be given, and any equivalent usage ratios that were used should be
Life-cycle inventory studies generate a great deal of information, often of a disparate
nature. The analyst needs to select a presentation format and content that are consistent
with the purpose of the study and that do not arbitrarily simplify the information solely
for the sake of presenting it. In thinking about presentation of the results, it is useful to
identify the various perspectives embodied in life-cycle inventory information. These
dimensions include, but may not be limited to, the following:
Overall product system
Relative contribution of stages to the overall system
Relative contribution of product components to the overall system
Data categories within and across stages, e.g., resource use, energy consumption, and
environmental releases
Data parameter groups within a category, e.g., air emissions, waterborne wastes, and
solid waste types
Data parameters within a group, e.g., sulfur oxides, carbon dioxide, chlorine, etc.
Geographic regionalization if relevant to the study, e.g., national versus global
Temporal changes.
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The life-cycle analyst must select among these dimensions and develop a presentation
format that increases comprehension of the findings without oversimplifying them. Two
main types of format for presenting results are tabular and graphical.
Sometimes it is useful to report total energy results while also breaking out the
contributions to the total from process energy and energy of material resources. Solid
wastes can be separated into post-consumer solid waste and industrial solid waste.
Individual atmospheric and water pollutants should be reported separately. Atmospheric
emissions, waterborne wastes, and industrial solid wastes can also be categorized by
process emissions/wastes and fuel-related emissions/wastes. Such itemized presentations
can assist in identifying and subsequently controlling certain energy consumption and
environmental releases.
The results from the inventory can be presented most comprehensibly in tabular form.
The choice of how the tables should be created varies, based on the purpose and scope of
the study. If the inventory has been performed to help decide which type of package to
use for a particular product, showing the overall system results will be the most useful
way to present the data. On the other hand, when an analysis is performed to determine
how a package can be changed to reduce its releases to the environment, it is important to
present not only the overall results, but also the contributions made by each component of
the packaging system. For example, in analyzing a liquid delivery system that uses plastic
bottles, it may be necessary to show how the bottle, the cap, the label, the corrugated
shipping box, and the stretch wrap around the boxes all contribute to the total results. The
user can thus concentrate improvement efforts on the components that make a substantial
contribution when evaluating proposed changes.
Graphical presentation of information helps to augment tabular data and can aid in
interpretation. Both bar charts (either individual bars or stacked bars) and pie charts are
valuable in helping the reader visualize and assimilate the information from the
perspective of “gaining ownership or participation in life-cycle assessment” (Werner
1991). However, the analyst should not aggregate or sum dissimilar data when creating or
simplifying a graph.
For internal industrial use by product manufacturers, pie charts showing a breakout by
raw materials, process, and use/disposal have been found useful in identifying waste
reduction opportunities.
For external studies, the data must be presented in a format that meets one fundamental
criterion - clarity. Ensuring clarity requires that the analyst ask and answer questions
about what each graph is intended to convey. It may be necessary to present a larger
number of graphs and incorporate fewer data in each one. Each reader should understand
the desired response after viewing the information.
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Once the data has been collected and organized into one format or another, the accuracy
of the results must be verified. The accuracy must be sufficient to support the purposes
for performing the LCA as defined in the goal and scope.
4. Interpretation of Data
Life cycle interpretation is a systematic technique to identify, quantify, check, and
evaluate information from the results of the LCI and the LCIA, and communicate them
effectively. Life cycle interpretation is the last phase of the LCA process.
International Standard Organization (ISO) has defined the following two objectives of
life cycle interpretation:
1. Analyze results, reach conclusions, explain limitations, and provide recommendations
based on the findings of the preceding phases of the LCA, and to report the results of
the life cycle interpretation in a transparent manner.
2. Provide a readily understandable, complete, and consistent presentation of the results
of an LCA study, in accordance with the goal and scope of the study.
The first step of the life cycle interpretation phase involves reviewing information from
the first three phases of the LCA process in order to identify the data elements that
contribute most to the results of both the LCI and LCIA for each product, process, or
service, otherwise known as “significant issues.”
The results of this effort are used to evaluate the completeness, sensitivity, and
consistency of the life cycle inventory analysis. The identification of significant issues
guides the evaluation step. Because of the extensive amount of data collected, it is only
feasible within reasonable time and resources to assess the data elements that contribute
significantly to the outcome of the results.
Before determining which parts of the LCI and LCIA have the greatest influence on the
results for each alternative, the previous phases of the LCA should be reviewed in a
comprehensive manner (e.g., study goals, ground rules, impact category weights, results,
external involvement, etc.). Review the information collected and the presentations of
results developed to determine if the goal and scope of the LCA study have been met. If
they have, the significance of the results can then be determined. Determining significant
issues of a product system may be simple or complex. For assistance in identifying
environmental issues and determining their significance, the following approaches are
Contribution Analysis - the contribution of the life cycle stages or groups of processes are
compared to the total result and examined for relevance.
Dominance Analysis - statistical tools or other techniques, such as quantitative or
qualitative ranking, are used to identify significant contributions to be examined for
Anomaly Assessment - based on previous experience, unusual or surprising deviations
from expected or normal results are observed and examined for relevance.
Significant issues can include:
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1. Inventory parameters like energy use, emissions, waste, etc.
2. Impact category indicators like resource use, emissions, waste, etc.
3. Essential contributions for life cycle stages to LCI or LCIA results such as individual
unit processes or groups of processes (e.g., transportation, energy production).
The evaluation step of the interpretation phase establishes the confidence in and
reliability of the results of the LCA. This is accomplished by completing the following
tasks to ensure that products/processes are fairly compared:
Completeness Check - examining the completeness of the study.
Sensitivity Check - assessing the sensitivity of the significant data elements that influence
the results most greatly.
Consistency Check - evaluating the consistency used to set system boundaries, collect
data, make assumptions, and allocate data to impact categories for each alternative.
The objective of this step is to interpret the results of the life cycle impact assessment
(not the life cycle inventory analysis) to determine which product/process has the overall
least impact to human health and the environment, and/or to one or more specific areas of
concern as defined by the goal and scope of the study.
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BATCH / SEMI-CONTINUOUS PLANTS (Stefanis et al., 1997)
A key characteristic of batch plants is their inherent operational flexibility in utilizing
available resources (equipment, utilities, production time). This feature introduces an
extra complexity in the design of such plants, since design considerations are interlinked
with operational/scheduling aspects. This, in turn, implies that waste generation in batch
plants depends on both design and scheduling decisions over a time horizon, related to
product sequencing, task scheduling, need for cleaning as well as type and sizes of
equipment. Another key issue for consistent environmental impact assessment is the need
to translate waste generation over time to some measure of environmental damage as well
as to account for input wastes (to the process) and their interactions with output waste
generation. Consider for instance the simplified process in Fig. 10, which is part of a
cottage cheese production chain (Crooks, 1992).
Fig. 10. Batch plant for motivating example.
Milk is mixed with culture in two vat processors to produce curd cheese, an intermediate
cheese state, and whey by-product. The curd yield depends on fat, casein and moisture
content of the input milk (Lucey and Kelly, 1994). The curd is then drained to produce
the required amount of cheese. Pollution is due to the organic matter in the drainer
effluent, which slowly oxidizes under bacterial and chemical action creating oxygen
demand. The Biological Oxygen Demand (BOD) of that waste water stream is increased
due to the skim milk intake loss (approximately equal to 4-5% weight) and strongly
depends on the input milk fat content, as confirmed by statistical analysis of experimental
values (Purnell and Flagg, 1984). A single campaign operating policy is assumed with a
cyclic four-hour schedule repeated over a production time of 5508 h/y in order to
generate 275.4 tons curd. Task information such as duration, type of input (I) and output
(O) states from each task and their corresponding mass fractions and unit characteristics
are given in Tables 1 and 2. For example, O Solcurd implies that Solidified Curd is an
output from the task Drain, it is produced 30 min after the task starts and its mass fraction
at the exit of the task is 0.9. Input milk costs £0.16 /kg, whereas the curd product is sold
at £0.655/kg.
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Table-1. Task information for motivating example.
TASK Duration (min) In-out state In-out time (min) In-out fraction
I culture 0 0.12
I milk 0 0.88
O Whey 240 x(F*)
Vat Proc 240
O Curd 240 1- x(F)
I curd 0 1.0
O Solcurd 30 0.9
Drain 30
O Waste Water 30 0.1
* % wt of milk fat
Table-2. Unit characteristics for motivating example
Capacity (kg) Costs
Minimum Maximum Fixed (k£) Variable (k£)
Vat 1 50 1100 75 0.45
Vat 2 50 1800 81 0.5
Drainer 5 225 45 0.3
Milk Silo - 14,100 15 0.1
Culture Silo - 10,000 15 0.1
Whey Tank - 10,000 15 0.1
Waste link - 10,000 15 0.1
Sol. Curd Tank - 10,000 15 0.1
Table-3. Batch plant design alternatives.
Case 1 Case 2
Units capacity (kg)
Vat 1 1100 1100
Vat 2 787 1476
Drainer 130 128
Milk Silo 1661 2267
Culture Silo 227 310
Whey Tank 1665 2353
Waste Tank 23 23
Sol. Curd Tank 200 200
Milk Fat Content (% wt)
Total Annual Cost (k£)
BOD of Effluent (kg/cycle)
Table 3 and Fig. 11 depict two design alternatives (and their schedules) corresponding to
the solution of the following cases:
Case 1: minimization of total annualised cost (TAC in £/y)
Case 2: minimization of BOD in the waste water stream (kg).
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Fig. 11. Optimal schedules and waste generation of motivating example.
A careful examination of these results reveals a number of interesting features:
Waste emissions are due to the operation of the drainer, which is operational only 25% of
the batch cycle time (see Fig. 11c); thus, unlike continuous plants in which waste output
can be quantified on an hourly basis, in batch plants the horizon time, the mode of
operation and the specific scheduling pattern over time, all should be simultaneously
taken into account in properly quantifying waste output (in some aggregated over time
On a waste mass discharge basis, the two designs are almost equivalent: i.e. 23 kg of
wastewater per cycle of operation. However, their environmental damage, translated into
an effluent BOD, is distinctly different (with 20% less BOD for Case 2, see Table-3).
Milk fat content is much lower in Case 2 compared to Case 1 (Table-3). This is achieved,
however, at the expense of increased requirements in raw material (milk requirements in
Case 2 are 2267 kg compared to 1661 kg in Case 1), since curd yield increases when milk
fat content increases. Additional raw material consumption results in general in an
increase of the input waste to the cottage cheese process, i.e. the waste associated with
the production of milk. Clearly, such trade-offs are important and must be taken into
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consideration if a consistent environmental impact assessment is desired. Trade-offs also
exist (as expected) between cost and environmental impact: Case 1 compares favorably to
Case 2 on economic grounds (25% less expensive) but poorly with respect to
environmental impact (20% more BOD). Furthermore, results indicate that waste
generation increases substantially for high fat input milk (3% wt. milk fat content results
approximately in 15% waste increase).
5.1. A methodology for environmental impact minimization of batch plants
As mentioned before, waste generation in batch a plant is a function of time and critically
depends on detailed scheduling/operational and design decisions. In this work we
consider the following problem.
A set of desired products (fixed amounts)
A set of raw material alternatives
Technology of raw material extraction processes
Regarding the batch process of interest; for example,
Campaign mode
Design alternatives
Task information (processing times, input – output details)
Cost data
Equipment cost
Raw material, product and utility prices
environmental data (from databases)
Maximum Acceptable Concentration Limits (in air, water)
Long Term Effect Potentials (such as Global Warming, Ozone Depletion and
The objective is to obtain the cost optimal, structural designs and operating policies of a
multipurpose batch process so as to minimize the adverse environmental effects on a
conventional or global basis.
The following methodology is thus proposed involving three main steps:
1. Definition of batch plant system boundary
2. Aggregated (over time) environmental impact assessment
3. Incorporation of environmental impact criteria in batch plant design/scheduling.
5.1.1. Definition of batch plant system bound(w)
The conventional system boundary of a batch plant can be expanded to include all
processes associated with raw materials extraction, energy generation and capital
manufacture. A global batch plant system boundary is defined by backtracking from the
conventional batch process all the way back to the natural state of pure raw materials
which are available at no environmental penalty. By defining such an expanded batch
plant system boundary, input wastes to the batch plant can be accounted together with
output emissions in a global waste vector.
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5.1.2. Aggregated environmental impact assessment
Having defined a global system boundary for the batch plant, an assessment of the
aggregated site-wide waste vector must be performed. This involves the following (see
Fig. 12):
Fig. 12. Aggregated environmental impact assessment in batch plants.
(Basis: one cycle of operation.)
(a) Defining a suitable time period as a basis for a consistent evaluation of the
environmental impact. If a campaign mode of batch operation is assumed, then the
cycle time T is used; otherwise, the horizon time H can be used instead.
(b) Defining an emissions inventory comprising all wastes generated in any stage of the
batch processing network within the global boundary of the batch plant of interest.
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(c) Grouping systematically these wastes in terms of the environmental damage caused
(air pollution, water pollution, global warming etc.). Assuming that there are no
post-release interactions among pollutants, an Environmental Impact Vector E1 per
time interval is defined to account for the fact that tasks generating waste do not
operate continuously over time. Therefore. for each unit to task allocation, the
indices which measure air pollution (CTAM, kg air), water pollution (CTWM, kg
water), solid wastes (SMD, kg solids), global warming (GWI kg CO2),
photochemical oxidation (POI, kg ethylene) and stratospheric ozone depletion
(SODI, kg CFC11) are expressed for each waste w emitted at time interval t, as
shown in Table 4. Note that these metrics depend on the current legislation limits
and the mass of pollutant disposed (expressed as a proportion of the unit batch size).
Table-4. Time dependent environmental impact indicator
(kg air)
(Habersatter, 1991)
Mass of emissions w at interval t (kg w)/standard limit value
(kg w/kg air)
(kg water)
(Habersatter, 1991)
Mass of pollutant w at interval t (kg w)/standard limit value
(kg w/kg water)
(kg solids)
(kg CO
(Lashof and Ahuja, 1990)
Mass of solid disposed at interval t (kg w/h)
Mass of pollutant w at interval t (kg w) x GWP (kg CO
(kg ethylene)
(UK Ecol. Board, 1993)
Mass of pollutant w at interval t (kg w) x POCP (kg C
(kg CFC11)
(UK Ozone Grp., 1998)
Mass of pollutant w at interval t (kg w) x SODP (kg
CFC11/kg w)
(d) Aggregating over time. For example, for cyclic operation the cycle time T
is used
as a basis for the quantification of Global Environmental Impact (GEI).
The environmental impact vector defined above, can be employed on a site wide as well
as on a process basis; it can also be used in conjunction with typical metrics such as
BOD, COD etc. Note also that the assumption of the linear pollutant contribution can be
relaxed by considering the fate of each pollutant in the environment by accounting for
pollutant interactions and pollutant media partitioning (Stefanis et al., 1996).
5.1.3. Incorporation of environmental impact criteria in the design and scheduling
of batch plants
In order to consider environmental criteria as distinct objectives together with cost in the
design and scheduling problem of batch plants, a multi-objective optimization
formulation is considered to generate the family of designs and the corresponding
operating policies that refer to the pareto curve of solutions trading-off cost versus
pollution metrics. Using the e-constraint method (Hwang and Masud, 1979), this can be
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effectively transformed into a scalar parametric optimization problem with the other
objectives added as inequality constraints, as follows:
(P) min Annualized Cost
Batch Plant Design Model
Scheduling Constraints
Varying the parameter vector ε results in the generation of the trade-off curve of solutions
(pareto curves, see Fig. 13) which can help analyze the environmental implications at
both the process and site-wide level and obtain compromise solutions. Note also that
batch process synthesis aspects are not directly included in problem (P) such
considerations would require detailed macro-level information regarding choice of
reaction, solvents, recipes, etc.
Fig. 13. Pareto curves in MEIM.
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In the present LCI, a product system of the hard coal electricity is presented which covers
all the life cycle stages - starting from the extraction of resources up to the delivery of
electric power to the grid. Since the OSELCA project is focused on the future, not on the
average Finnish situation, the Finnish Meri-Pori power plant, representing the best
available power plant technology, was chosen as a case study power plant. Although the
Meri-Pori obtains its fuel from many different countries, inventory data on the coal
mining was collected using the average interventions of coal mining in Poland, as Poland
is one of the largest suppliers of hard coal to Finland. In this report LCI results are
analysed briefly. The current inventory analysis concentrates on the quantitatively
measured emissions.
Division of the product system into different life cycle stages facilitates the interpretation
of the LCI results. It enables one to evaluate and compare the relative contributions of the
interventions between the stages. How the life cycle stages are defined depends on the
purpose of the study. In this inventory, the LCI data was compiled according to the
following six life cycle stages (see Fig. 14):
1. Hard coal mining and processing
2. Raw materials to coal mines and the power plant
3. External electricity and heat generation in Poland (coal mining and its raw materials)
and in Finland (power plant and its raw materials)
4. Hard coal power plant
5. Transportation
6. Recovered wastes, treated as by-products
All these life cycle stages are shown on the flowchart with different colors. The software
calculates the inventory results per the functional unit (1MWh electricity) by the
modules, the life cycle stages or for the system as a whole. In the flowchart the mining
modules are marked with grey color. They include mining and processing of the hard
coal, also manufacturing and burning of fuels used in the mine. The raw material modules
are marked with light green. They cover the production of raw materials for both mining
and power plant. The external electricity and heat generation in Poland is marked with
dark green and in Finland with turquoise. The power plant module is colored with purple.
This module includes also the final product - i.e. 1 MWh of electricity at grid – including
average transmission losses in Finland. The transportation modules are painted blue and
shown as short diagonal lines, which cross the arrows. Transportation was not taken into
account for all the raw materials or by-products. Indirect (from the production of fuels)
and direct emissions together constitute summarized input and output factors, used in the
transportation modules. Credited by-product modules (i.e. avoided emissions) are marked
with dark lilac color. These contain waste materials from the mine or the power plant that
were utilized in other production processes to replace virgin raw materials. In the
following section the modules of the product system and their data sources are described
in detail from the viewpoint of the two main processes hard coal mining in Poland and
electricity generation from hard coal in Finland - with all their material and energy
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inputs. The Polish and Finnish electricity generation models, used for manufacturing of
raw materials. The LCI results are, however, presented according to the life cycle stages.
Fig. 14. Flow chart of the hard coal electricity product system.
In this inventory, electricity generation with hard coal was assessed. For this,
environmental inputs and outputs throughout the product life cycle, i.e. from hard coal
mining to the electrical power network, were gathered. Whereas the hard coal production
took place in Poland and mining data was assumed to be average Polish mining
technology, the electricity generation was assumed to take place in Finland, at the Meri-
Pori power plant. In the following sections, summary of the inventory results is
6.1. Inputs and outputs per 1 MWh electricity produced
Summary results of the main emissions to air from the system are shown in Table 5. The
heavy metal emissions to the atmosphere occur mainly at the power plant (Fig. 15). It
produces 57-95% of total heavy metal emissions except for Hg, the share of which was
11%. Also Polish electricity generation (mainly used in the mining operations) is a large
contributor to the heavy metal emissions. It produces most (~63%) of the Hg emissions
and over 5-25% of the other metal emissions. Transportation of hard coal within Poland
by electric trains also produces about 17% of the Hg emissions.
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Table-5. Some emissions to air from the product system.
Fig. 15. Atmospheric emissions of some heavy metals (g/MWh). The abbreviations of
the life cycle stages are explained in Table-14.
6.2. Inputs and outputs according to the life cycle stages
The life cycle stages of the studied product system are following: mining, external
electricity and heat generation in Poland, production of raw materials for both mining and
power plant, power plant processes, transportation, external electricity and heat
generation in Finland, and saved external processes. Approximately 95% of all the CO
emissions of the whole system originate from the power plant (Figure 16). The power
plant also causes most of the NO
and SO
emissions (Figs. 17 and 18). In addition,
transportation of the hard coal from the mines to the power plant induces a great deal of
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emissions - 30% of the total emissions. It should be noted that CO
, NO
and SO
emissions are not measured at the mines. Therefore emissions from burning fuels at the
mine area were included to the flow chart from the Ecoinvent database. Their
contribution to the total emissions is minimal, however. As could be expected, CH
originates mainly from the mines (Fig. 19). The contribution of the other life cycle stages
to the CH
emissions is insignificant.
Fig. 16. CO
emissions according to the life cycle stages
Fig. 17. NO
emissions according to the life cycle stages
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Fig. 18. SO
emissions according to the life cycle stages
Fig. 19. CH
emissions according to the life cycle stages
(Abbreviations: MI = Mining, PL = Polish electricity and heat generation, RM = Raw materials,
PP = Power plant, TP =Transportation, FI = Finnish electricity and heat generation and
AE = Saved external processes)
In this product system, most of the particle emissions originate from the production of
limestone, which is used to reduce SO
emissions from the power plant (Fig. 20). On the
other hand, replacement of cement through fly ash recycling produces an approximately
4% reduction in particle emissions through saved limestone.
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Fig. 20. Particle emissions according to the life cycle stages
6.3. Waste generation and land use
The largest waste fraction of the system, 109.1 kg/MWh, is created during processing of
hard coal. Mining (excavation) generates about 7.5 kg waste/MWh. Also fly ash and
gypsum produced at the power plant are large waste fractions. Waste generation during
the raw material production is minimal compared to mining and power plant.
Additionally, the amount of avoided waste from the saved external processes is larger
than the amount of waste generated by raw material production (Table-6).
Table-6. Waste generation according to the life cycle stages.
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Land use directly related to hard coal mining and energy conversion is presented in
Table-7. It will be dealt with more extensively in the impact assessment, especially land
use of mining activities. According to the present data, land use for mining appears to be
surprisingly small compared to the land use by the power plant. Possible differences in
calculation procedures and boundaries will be checked at a later stage of the project. It
should be noted that, the power plant waste disposal area is an area that has been reserved
for the power plant but so far it has not been necessary to dispose any waste there.
Table-7. Land usage for the mining and power plant operations.
6.4. Sensitivity Analysis
The sensitivity of the system to changes in four different factors, namely hard coal
transportation distance (Case 1), power plant technology (Case 2), external electricity and
heat generation (Case 3) and use of fl y ash in concrete manufacturing (Case 4) was
analysed (Fig. 21). In each case, the variable in question was varied while everything else
was held constant. In the following the four cases will be discussed.
Fig. 21. The sensitivity of the main emissions to air to the four
different factors studied
6.5. Hard coal transportation distance
In this inventory, the hard coal is assumedly transported from Poland. However, the
Meri-Pori power plant could also use hard coal transported from regions further away -
such as from South America or South-East Asia. The impact of the much longer
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transportation distance on emissions to air was investigated (Fig. 21, Case 1). Instead of
transportation from Poland to Meri-Pori by electric train and ferry, hard coal was
assumedly transported with ocean freight ferries from Colombia or Indonesia to Meri-
Pori. Emissions are calculated by Koskela (2002). Transportation distance was estimated
to be 10,000 km (average distance from Colombia or Indonesia to Finland). Train
transportation was omitted. Increasing transportation distance would lead to almost
tripling of the NO
emissions (see Fig. 21). SO
emissions would rise by ca 70% and
particle emissions by ca 30%. Thus, it can be concluded that hard coal transportation
distance significantly influences NO
, SO
and particle emissions of the whole system.
6.6. Power plant technology
Meri-Pori power plant represents the Best Available Technology (BAT) for the power
plant and its emissions are therefore relatively low. In order to see how an older power
plant would perform, inventory results were calculated for another power plant with less
advanced emission reduction technology for Tahkoluoto power plant, which is situated
right next to Meri-Pori plant (Fig. 21, Case 2). Data on Tahkoluoto power plant were
taken from Vahti database. As no information was available over the raw material
consumption of Tahkoluoto (except for the hard coal and heavy fuel oil), it was assumed
to be the same as that of Meri-Pori. This assumption is probably not realistic but as the
role of the power plant raw materials for the whole inventory was proved to be insignifi
cant, this assumption was considered justified. Fig. 21 shows the results of the
comparison. For all substances, Tahkoluoto’s emissions were higher than those of Meri-
Pori. For NO
and SO
the difference was 170% and 45%, respectively, which refl ects
Meri-Pori’s advanced emission reduction systems. It is notable that if hard coal also was
transported to Tahkoluoto from overseas (Case 1), the increase in NO
, SO
and particle
emissions would almost double.
6.7 External electricity and heat generation
Over 90% of the Polish electricity is generated from hard or brown coal. More than 50%
of the Finnish electricity is generated from nuclear power, hydropower and natural gas,
so it is therefore not so emission-intensive. In order to study the impact of Polish
electricity and heat generation on the total emissions of the production system, the results
were recalculated with the assumption that all external electricity and heat were generated
in Finland. As seen from the Fig. 21 (Case 3), the change in total emissions would be
insignifi cant in this product system - CO
, NO
and CH
emissions would decrease by 2-
4%. However, particle and SO
emissions would decrease by 10 and 11% respectively.
6.8 Use of fly ash in cement manufacturing
Cement production is energy intensive and highly polluting. The fly ash from hard coal
power plants can be utilised in concrete manufacturing to replace cement. Current
utilization rate of the Meri-Pori plant’s flying ash in cement manufacturing is 5% while
the rest is used for earth construction works. Considerable emission reductions could be
achieved by increasing the recycling rate. Fig. 21 (Case 4) shows emission reductions,
resulting from increasing the recycling rate up to 80% (the rest would still be utilized in
earth construction). The increased recycling rate would have the greatest impact on total
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particle emissions, which would be reduced by approximately 30%. For the other
emissions the reduction would range from 1.5 (CH
) to 8% (NO
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Karaliūnaitė, 2006)
7.1. Introduction of the problem
The disposal of used tyres is an environmental problem nowadays. The challenge of scrap
tyre management arises mainly from the technical and commercial issues relating to tyres
both as a product and as a waste. Tyres are made from a mixture of materials including
synthetic and natural rubber, textiles, steel, carbon black, aromatic extender oils and
various chemical additives, which are “vulcanised” at a high temperature during the
manufacturing process. The result is a particularly stable product that requires a great
deal of energy to break it down. For instance, tyres are more difficult to combust than
conventional fuels (even though the energy content is higher than that of most coals and
similar to that of natural gas), and therefore tyres require higher temperatures and/or
longer residence times to promote a complete breakdown of the hydrocarbon content into
carbon dioxide and water. A significant amount of energy is also needed to mechanically
reduce the size of tyres, in order to produce materials that are suitable to be recycled into
engineering, commercial or industrial products. So while tyres represent a feedstock with
a high energy content, which contain potentially valuable constituents such as carbon
black, organic oils and steel, extracting these materials in a cost-effective manner is
extremely difficult (Archer, 2004).
Scrap vehicle tyres make a significant contribution to the generation of waste. The rate of
scrap tyre generation in EU countries is approximately 7 kg per capita (~9 kg per capita
in the USA) (Reschner, 2003; Staniškis, 2004). It is estimated that 2.8 million tonnes of
used tyres per year are generated in the EU member states 2.5 million tonnes in North
America and 1 million tonnes in Japan. Lithuanian production of waste tyres should be
up to17 000 tonnes per year considering, that the annually generated amount of end-of-
life tyres is a difference between the import and export of tyres. In 1999-2000 in EU three
Directives were enacted regarding post-consumer tyres. The EU Landfill Directive
(Council, 1999) banned the landfilling of whole tyres from 2003 and will ban the
landfilling of shredded tyres in 2006. Moreover, the indirect impact of the European End
The Waste Incineration Directive was adopted with the aim of preventing or limiting
emissions from incineration and co-incineration of waste (Directive, 2000). The Directive
sets more stringent emission standards for a number of pollutants including dust, HCl,
HF, NOx, dioxins and heavy metals. Since thermal recovery in cement kilns and power
plants is one important route for disposal of scrap tyres, the Waste Incineration Directive
may compel some current users of tyre-derived fuel to refurbish their emission control
systems. It is estimated that all the above-mentioned legal restrictions will incur an
additional amount of over 1 million tonnes of scrap tyres requiring appropriate treatment
in the EU. At the moment, the most significant methods and technologies developed for
waste tyre recovery and/or disposal are:
Reusing in the original form
Retreading of worn tyres
Shredding operations to get a powdered or scrapped form
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Thermal treatments to perform material and/or energy recovery
Utilisation in building applications; other treatments
Landfilling, heaping and abandonment
7.2. Object of the study
The treatment of scrap tyres is associated with diverse potential environmental impacts.
From a very general point of view, a significant effect is caused due to the material flow
within an industrial society. The protection of natural resources is likely to be the main
ecological justification for recovery of used tyres. The main objective of the study was to
evaluate and compare five different end-of-life tyre treatment technologies and their
environmental impacts.
Five different technologies for recovery of used tyres were analysed in this study:
1. Co-incineration in cement kiln.
2. Thermolysis.
3. Mechanical recycling (conventional).
4. Baro-destructive recycling.
5. Mechanical recycling (ultrasound).
7.3. Methodology
The environmental impact assessment methodology and data were chosen as the main
source of initial inventory data, because only one of the analyzed technologies is already
in operation in Lithuania; the other technologies are still in the implementation process,
but EIA has been conducted for all of them.
7.3.1. Functional unit and system boundaries
One of the most important elements in a LCA study is a clear description of the system’s
function and, derived from it, the functional unit for the study. In comparative studies, it
is essential that the systems are compared on the basis of the same function. In the
present study, the functional unit was considered to be the recovery of 1 tonne of end-
oflife tyres. Another important issue is the definition of system boundaries. A life cycle
process diagram of tyres is presented in Fig. 22. The definition of the tyre recovery
system boundaries has been based on the assumption that production, use and collection
stages in tyre life cycle are equivalent, therefore these processes were not included in the
study. However, evaluation of five different technologies of scrap tyre recovery
necessitated collection of quite a number of different data and information. Several more
assumptions had to be made in relation to the analysed technologies:
It’s very difficult to distinguish air emissions caused by the combustion of a tyre
from the ones caused by the cement production process. At this stage of the
research, air emissions of tyre combustion in the cement kiln were separated from
the process air emission flow and were estimated according to the emission rate
from the combustion of a tyre alone. This decision was based on the choice of the
worst case; • in case of co-incineration in the cement kiln, energy consumption for
lifting the tyres into the feeder was not evaluated, as these data were not available.
There has been made an assumption that the calorific value of thermolysis gas is
equal to the calorific value of natural gas in calculations of
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energy balance.
The definition of products is based on an assumption that all materials generated
during the recovery process are considered as products if they have any practical
All calculations were based on the assumption that used tyres were of average
quality and composition and that the analyzed technologies were of an average to
advanced level for tyre waste treatment.
Fig. 22. Tyre life cycle diagram and boundaries of life cycle analysis.
7.3.2. Life cycle inventory analysis
The first step in life cycle assessment is an inventory analysis, which includes and
quantifies material and energy use and emissions to the environment (EN ISO, 1998).
This article is aimed to present only the results of this LCA phase. Primary data on
the inputs and outputs were taken from EIA carried out in advance to estimate the
local impact of the recovery systems chosen for this study. The data used in IAE were
calculated according to appropriate methodologies adopted in Lithuania or modeled
according to the method of analogy, i.e. estimated on the basis of analogous
technologies used in other countries. Data gaps were filled with information from
supplementary technical documents and other literature sources
7.4. Results and Discussion
Environmental impact assessment was conducted for all analyzed technologies, and
the proposed economic activities by virtue of their nature and environmental impacts
can be executed in the chosen sites in Lithuania. The life cycle inventory analysis of
Life Cycle Inventory Analysis (LCIA)
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end-of-life tyre recovery was performed according to the LCA methodology (EN
ISO, 1998). The general view of the main input and output data is outlined in Fig. 23.
All inventory data are expressed per functional unit.
Fig. 23. Inventory analysis – the main input and output data
Mechanical recycling, thermolysis and coincineration of waste tyres in a cement kiln
produce different products: energy, gas and liquid fuels, rubber, metal, textile and
technical carbon. A comparison of the material outputs was made on the basis of
mass yield (Fig. 24). Energy output of coincineration of tyres is presented in Fig. 25.
As it cannot be expressed in mass parameters, it is compared with energy output that
can be gained during the combustion of thermolysis products gas and liquid fuels.
Comparison of energy consumption and production of all analysed technologies (Fig.
26) shows that the highest demand of electric power is needed by the baro-destructive
method of recycling of used tyres, while the highest amount of heat energy is
generated during direct tyre co-incineration. Water is used in three analysed
technologies: for steam production in the thermolysis process, for tyre watering in
conventional mechanical recycling, and for dust removal in the scrubber in the
ultrasound recycling process.
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Life Cycle Assessment & Life Cycle Management Methodologies
Fig. 24. Material outputs per functional unit (from recovery of 1 tonne of tyres):
1 – co-incineration in cement kiln; 2 – thermolysis; 3 – mechanical recycling; 4 –
baro-destructive recycling; 5 – mechanical recycling
Fig. 25. Comparison of energy consumption and production per functional unit
(recovery of 1 tonne of tyres)
Life Cycle Inventory Analysis (LCIA)
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Fig. 26. Energy output from co-incineration of 1 tonne of scrap tyres in cement
kiln and potential energy output from thermolysis of combustible products from
1 ton of tyres
Water consumption is relatively low in all technological processes (Fig. 27), although
water re-circulation is applied only in thermolysis. It should be mentioned that no
industrial wastewater is discharged in either of the cases. Pseudo-latex pulp from
scrubber of ultrasound technology is landfilled as well as dust from fabric filters of the
barodestructive method (Fig. 28). The dust collected in the electrostatic precipitator in a
cement plant as well as in fabric filters of conventional mechanical recycling is returned
back into the processes.
Co-incineration of tyres in cement kiln generates the biggest amount of direct air
emissions compared with the other analysed technologies of crap tyre treatment.
However, air emissions from the cement kiln co-incinerating scrap tyres insignificantly
differ from emissions generated by the combustion of main fuel (pulverised coal):
NOx emissions decrease approximately by
SO2 emissions may increase up to 10%;
Possible peaks of CO emissions (at the moment of whole tyre feeding into the
Dust emissions may increase by 15–20%.
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Fig. 27. Water consumption per functional unit (recovery of 1 tonne of tyres)
Fig. 28. Waste generation per functional unit (recovery of 1 tonne of tyres)
Life Cycle Inventory Analysis (LCIA)
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8. Conclusions
The following conclusion are drawn from the present study.
Life Cycle Inventory Analysis is found to be the foundation for the Life Cycle
Inventory analysis required the thorough understanding of subsystems.
Flow diagrams are used to model and compare all alternatives under consideration.
Data collection step is very important step in LCIA to determine how much of the
energy and material requirements and the environmental releases associated with the
The economic input output approach is quickly covers an entire economy, including
all the material and energy inputs.
The results from the inventory can be presented most comprehensibly in tabular form
or graphical form.
The identification of significant issues is possible by interpreting the inventory
A process systems methodology for incorporating environmental concerns in the
optimal scheduling and design of batch processes has been presented in case study-1.
The proposed methodology identifies waste generation sources within a batch plant
and after establishing relationships to link output to input waste generation transforms
it into an aggregated over time and species environmental impact vector.
The heavy metal emissions to the atmosphere occur mainly at the power plant.
It produces 57-95% of total heavy metal emissions except for Hg, the share of which
was 11%.
Approximately 95% of all the CO2 emissions of the whole system originate from the
power plant.
Transportation of the hard coal from the mines to the power plant induces a great deal
of NOX emissions - 30% of the total emissions.
The baro-destructive method of used tyre recycling requires the highest amount of
electric power (522 kWh per 1 tonne of scrap tyres to be recovered).
While the highest amount of heat energy is generated during direct tyre co-
incineration in a cement kiln (energy recovery of 1 tonne of scrap tyres equals to
9304 kWh).
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... Inventory analysis is an important part of LCA. It is a systematic, objective, stepwise procedure for quantifying energy and raw materials requirement, atmospheric emissions, water borne emissions, solid wastes, and other releases for the entire life cycle of a product, package process, material or activity (Babu 2006). ...
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There are different approaches to implement sustainability and Design for Sustainability (DfS) is the one that give more accurate result by considering both global and regional scales. Integration of Life Cycle Assessment (LCA) into Product Lifecycle Management (PLM) is an example of tool integration to support sustainability. In LCA framework, Life Cycle Inventory (LCI) is the quantified and classified list of input and output flow of the LCA model that is a model of the product system, linking the technological system to the ecosphere (Environment system). As each region has a unique environmental system, design characteristics and specifications of technological system should be modified and adopted based on these differences. Implementation of this approach will require geographical information of interacted environmental systems, which is a kind of new strategy in DfS. Therefore, we tested the interest of the integration of Geographical Information Systems (GIS) with PLM to support geographical considerations during product development activities. The main research question of this research work is then how to propose this PLM-GIS integration for DfS. Thus, we conducted that literature review on existing data models about product, environment, geography and their combination is a key to prove the link among them. Later the state of art highlighted the lack of a comprehensive product model integrated with geography and environment models, which could enable to support DfS. In the beginning of chapter 4 through section 4.1, case study of a simple flash light is proposed to identify the details on the relation between product and environment with geographic data classes. Later in section 4.2 a comprehensive data model is proposed, which could integrate product data with geography, through environmental impacts. Finally, in the last section of chapter 4, we discuss proposed data model in details with a defined scope on material aspect. In chapter 5, data model illustration of two existing desalination systems for two regions are analyzed to highlight how geographical differences could change product structure specifications.
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Environmental life cycle assessment is often thought of as cradle to grave and therefore as the most complete accounting of the environmental costs and benefits of a product or service. However, as anyone who has done an environmental life cycle assessment knows, existing tools have many problems: data is difficult to assemble and life cycle studies take months of effort. A truly comprehensive analysis is prohibitive, so analysts are often forced to simply ignore many facets of life cycle impacts. But the focus on one aspect of a product or service can result in misleading indications if that aspect is benign while other aspects pollute or are otherwise unsustainable. This book summarizes the EIO-LCA method, explains its use in relation to other life cycle assessment models, and provides sample applications and extensions of the model into novel areas. A final chapter explains the free, easy-to-use software tool available on a companion website. ( The software tool provides a wealth of data, summarizing the current U.S. economy in 500 sectors with information on energy and materials use, pollution and greenhouse gas discharges, and other attributes like associated occupational deaths and injuries. The joint project of twelve faculty members and over 20 students working together over the past ten years at the Green Design Institute of Carnegie Mellon University, the EIO-LCA has been applied to a wide range of products and services. It will prove useful for research, industry, and in economics, engineering, or interdisciplinary classes in green design. © 2006 by Resources for the Future. All rights reserved. All rights reserved.
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Life Cycle Inventory Analysis (LCIA) is a part of the Life Cycle Assessment (LCA) and is a thorough procedure accounting for the environmental loads during the product's life cycle. LCA is an approach to analyze the environmental implications of product and service systems. An indigenous adsorbent is prepared in the laboratory from sawdust using chemical-thermal treatment mechanism. A close analysis of the several activities involved in the process revealed that, the raw material, i.e., sawdust is being converted into an adsorbent, i.e., product. In other words, conversion of a material from one form into another is taking place. Simultaneously, a useful product is being obtained that is used in another activity. LCIA based on material balance approach is applied for the primary data generated in the laboratory for the preparation of the adsorbent using sawdust. Few suggestions are floated for minimizing the waste generated in the process. It is found that the LCIA can be successfully applied in the adsorption studies to assess the material flow changes and identify the options for minimizing the waste emissions and for reducing the load on natural resources.
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The paper analyses five end-of-life tyre treatment technologies – co-incineration in cement kiln, thermolysis, and three alternatives of mechanical recycling: conventional, baro-destructive and ultrasound mechanical recycling methods. Methodologies of environmental impact assessment and life cycle assessment were chosen for evaluation of environmental impacts caused by these technologies. Results of the preliminary life cycle inventory analysis based on data of environmental impact assessment are presented. Alternative end-of-life tyre treatment technologies were compared and all life cycle inventory data were recalculated per functional unit, which was defined as recovery of 1 tonne of end-of-life tyres. Key words: waste management, recycling, recovery, end-of-life tyres, life cycle assessment, inventory analysis, environmental impact assessment.
As the pressures on the chemical and process industries to improve their environmental performance are increasing, the need to move away from narrow system de®nitions and concepts in environmental system management is becoming more apparent. Life Cycle Assessment (LCA) is gaining wider acceptance as a method that enables quanti®cation of environmental interventions and evaluation of the improvement options throughout the life cycle of a process, product or activity. Historically, LCA has mainly been applied to products; however, recent literature suggests that it can assist in identifying more sustainable options in process selection, design and optimisation. This paper reviews some of these newly emerging applications of LCA. A number of case studies indicate that process selection must be based on considerations of the environment as a whole, including indirect releases, consumption of raw materials and waste disposal. This approach goes beyond the present practice of choosing Best Practicable Environmental Option (BPEO), by which it is possible to reduce the environmental impacts directly from the plant, but to increase them elsewhere in the life cycle. These issues are discussed and demonstrated by the examples of end-of-pipe abatement techniques for SO 2,NO x and VOCs and processes for the production of liquid CO 2 and O 2. The integration of LCA into the early stages of process design and optimisation is also reviewed and discussed. The approach is outlined and illustrated with real case studies related to the mineral and chemical industries. It is shown that a newly emerging Life Cycle Process Design (LCPD) tool offers a potential for technological innovation in process concept and structure through the selection of best material and process alternatives over the whole life cycle. The literature also suggests that LCA coupled with multi-objective optimisation (MO)
A systematic methodology for incorporating ecological considerations in the optimal design and scheduling of batch/semi-continuous processes is presented in this paper. The methodology embeds principles from Life Cycle Analysis (LCA) within a general multi-objective formulation for the design of multipurpose batch plants, with process economics and environmental impact as distinct design objectives. An expanded boundary is defined around the process of interest, for the consistent evaluation of environmental impact, which is quantified by a set of metrics (for air, water pollution, global warming etc.). Examples from the dairy industry are presented to demonstrate the potential of the methodology to assist in arriving at environmentally friendly and economically favourable batch designs and schedules. Issues regarding the use of alternative cleaning and legislation policies on batch operation and design are also discussed.